from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""marian"""
   __lowerCAmelCase           =				["""past_key_values"""]
   __lowerCAmelCase           =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







   def __init__(     self						:						int   ,   lowerCamelCase_						:						Optional[int]=5_8101   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						Dict=1024   ,   lowerCamelCase_						:						str=12   ,   lowerCamelCase_						:						List[Any]=4096   ,   lowerCamelCase_						:						str=16   ,   lowerCamelCase_						:						Tuple=12   ,   lowerCamelCase_						:						Dict=4096   ,   lowerCamelCase_						:						str=16   ,   lowerCamelCase_						:						Tuple=0.0   ,   lowerCamelCase_						:						str=0.0   ,   lowerCamelCase_						:						List[Any]=True   ,   lowerCamelCase_						:						Dict=True   ,   lowerCamelCase_						:						Optional[Any]="gelu"   ,   lowerCamelCase_						:						List[str]=1024   ,   lowerCamelCase_						:						Optional[Any]=0.1   ,   lowerCamelCase_						:						List[Any]=0.0   ,   lowerCamelCase_						:						Optional[Any]=0.0   ,   lowerCamelCase_						:						Union[str, Any]=0.0_2   ,   lowerCamelCase_						:						Optional[int]=5_8100   ,   lowerCamelCase_						:						List[str]=False   ,   lowerCamelCase_						:						Optional[Any]=5_8100   ,   lowerCamelCase_						:						List[Any]=0   ,   lowerCamelCase_						:						Dict=0   ,   lowerCamelCase_						:						Dict=True   ,   **lowerCamelCase_						:						List[str]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      vocab_size
    UpperCamelCase			  =      decoder_vocab_size or vocab_size
    UpperCamelCase			  =      max_position_embeddings
    UpperCamelCase			  =      d_model
    UpperCamelCase			  =      encoder_ffn_dim
    UpperCamelCase			  =      encoder_layers
    UpperCamelCase			  =      encoder_attention_heads
    UpperCamelCase			  =      decoder_ffn_dim
    UpperCamelCase			  =      decoder_layers
    UpperCamelCase			  =      decoder_attention_heads
    UpperCamelCase			  =      dropout
    UpperCamelCase			  =      attention_dropout
    UpperCamelCase			  =      activation_dropout
    UpperCamelCase			  =      activation_function
    UpperCamelCase			  =      init_std
    UpperCamelCase			  =      encoder_layerdrop
    UpperCamelCase			  =      decoder_layerdrop
    UpperCamelCase			  =      use_cache
    UpperCamelCase			  =      encoder_layers
    UpperCamelCase			  =      scale_embedding  # scale factor will be sqrt(d_model) if True
    UpperCamelCase			  =      share_encoder_decoder_embeddings
    super().__init__(
        pad_token_id=lowerCamelCase_   ,   eos_token_id=lowerCamelCase_   ,   is_encoder_decoder=lowerCamelCase_   ,   decoder_start_token_id=lowerCamelCase_   ,   forced_eos_token_id=lowerCamelCase_   ,   **lowerCamelCase_   ,   )





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    if self.task in ["default", "seq2seq-lm"]:
     UpperCamelCase			  =      OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
         ]				)

     if self.use_past:
      UpperCamelCase			  =      {0: """batch"""}
      UpperCamelCase			  =      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
     else:
      UpperCamelCase			  =      {0: """batch""", 1: """decoder_sequence"""}
      UpperCamelCase			  =      {0: """batch""", 1: """decoder_sequence"""}

     if self.use_past:
      self.fill_with_past_key_values_(lowerCamelCase_   ,   direction="""inputs"""				)
    elif self.task == "causal-lm":
     # TODO: figure this case out.
     UpperCamelCase			  =      OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
         ]				)
     if self.use_past:
      UpperCamelCase  ,     UpperCamelCase			  =      self.num_layers
      for i in range(lowerCamelCase_				):
       UpperCamelCase			  =      {0: """batch""", 2: """past_sequence + sequence"""}
       UpperCamelCase			  =      {0: """batch""", 2: """past_sequence + sequence"""}
    else:
     UpperCamelCase			  =      OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
             ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
         ]				)

    return common_inputs







   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    if self.task in ["default", "seq2seq-lm"]:
     UpperCamelCase			  =      super().outputs
    else:
     UpperCamelCase			  =      super(lowerCamelCase_   ,   self				).outputs
     if self.use_past:
      UpperCamelCase  ,     UpperCamelCase			  =      self.num_layers
      for i in range(lowerCamelCase_				):
       UpperCamelCase			  =      {0: """batch""", 2: """past_sequence + sequence"""}
       UpperCamelCase			  =      {0: """batch""", 2: """past_sequence + sequence"""}
    return common_outputs







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						PreTrainedTokenizer   ,   lowerCamelCase_						:						int = -1   ,   lowerCamelCase_						:						int = -1   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						Optional[TensorType] = None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self._generate_dummy_inputs_for_encoder_and_decoder(
        lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)

    # Generate decoder inputs
    UpperCamelCase			  =      seq_length if not self.use_past else 1
    UpperCamelCase			  =      self._generate_dummy_inputs_for_encoder_and_decoder(
        lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)
    UpperCamelCase			  =      {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
    UpperCamelCase			  =      dict(**lowerCamelCase_   ,   **lowerCamelCase_				)

    if self.use_past:
     if not is_torch_available():
      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
     else:
      import torch
     UpperCamelCase  ,     UpperCamelCase			  =      common_inputs["""input_ids"""].shape
     UpperCamelCase			  =      common_inputs["""decoder_input_ids"""].shape[1]
     UpperCamelCase  ,     UpperCamelCase			  =      self.num_attention_heads
     UpperCamelCase			  =      (
         batch,
         num_encoder_attention_heads,
         encoder_seq_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )
     UpperCamelCase			  =      decoder_seq_length + 3
     UpperCamelCase			  =      (
         batch,
         num_decoder_attention_heads,
         decoder_past_length,
         self._config.hidden_size // num_decoder_attention_heads,
     )

     UpperCamelCase			  =      torch.cat(
         [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCamelCase_   ,   lowerCamelCase_				)]   ,   dim=1				)

     UpperCamelCase			  =      []
     # If the number of encoder and decoder layers are present in the model configuration, both are considered
     UpperCamelCase  ,     UpperCamelCase			  =      self.num_layers
     UpperCamelCase			  =      min(lowerCamelCase_   ,   lowerCamelCase_				)
     UpperCamelCase			  =      max(lowerCamelCase_   ,   lowerCamelCase_				) - min_num_layers
     UpperCamelCase			  =      """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

     for _ in range(lowerCamelCase_				):
      common_inputs["past_key_values"].append(
          (
              torch.zeros(lowerCamelCase_				),
              torch.zeros(lowerCamelCase_				),
              torch.zeros(lowerCamelCase_				),
              torch.zeros(lowerCamelCase_				),
          )				)
     # TODO: test this.
     UpperCamelCase			  =      encoder_shape if remaining_side_name == """encoder""" else decoder_shape
     for _ in range(lowerCamelCase_   ,   lowerCamelCase_				):
      common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase_				), torch.zeros(lowerCamelCase_				))				)
    return common_inputs







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						PreTrainedTokenizer   ,   lowerCamelCase_						:						int = -1   ,   lowerCamelCase_						:						int = -1   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						Optional[TensorType] = None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self._generate_dummy_inputs_for_encoder_and_decoder(
        lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)

    if self.use_past:
     if not is_torch_available():
      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
     else:
      import torch
     UpperCamelCase  ,     UpperCamelCase			  =      common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     UpperCamelCase			  =      seqlen + 2
     UpperCamelCase  ,     UpperCamelCase			  =      self.num_layers
     UpperCamelCase  ,     UpperCamelCase			  =      self.num_attention_heads
     UpperCamelCase			  =      (
         batch,
         num_encoder_attention_heads,
         past_key_values_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )

     UpperCamelCase			  =      common_inputs["""attention_mask"""].dtype
     UpperCamelCase			  =      torch.cat(
         [common_inputs["""attention_mask"""], torch.ones(lowerCamelCase_   ,   lowerCamelCase_   ,   dtype=lowerCamelCase_				)]   ,   dim=1				)
     UpperCamelCase			  =      [
         (torch.zeros(lowerCamelCase_				), torch.zeros(lowerCamelCase_				)) for _ in range(lowerCamelCase_				)
     ]
    return common_inputs







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						PreTrainedTokenizer   ,   lowerCamelCase_						:						int = -1   ,   lowerCamelCase_						:						int = -1   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						Optional[TensorType] = None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      compute_effective_axis_dimension(
        lowerCamelCase_   ,   fixed_dimension=OnnxConfig.default_fixed_batch   ,   num_token_to_add=0				)

    # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
    UpperCamelCase			  =      tokenizer.num_special_tokens_to_add(lowerCamelCase_				)
    UpperCamelCase			  =      compute_effective_axis_dimension(
        lowerCamelCase_   ,   fixed_dimension=OnnxConfig.default_fixed_sequence   ,   num_token_to_add=lowerCamelCase_				)

    # Generate dummy inputs according to compute batch and sequence
    UpperCamelCase			  =      [""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
    UpperCamelCase			  =      dict(tokenizer(lowerCamelCase_   ,   return_tensors=lowerCamelCase_				)				)
    return common_inputs







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						PreTrainedTokenizer   ,   lowerCamelCase_						:						int = -1   ,   lowerCamelCase_						:						int = -1   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						Optional[TensorType] = None   ,   ):





    """simple docstring"""


    if self.task in ["default", "seq2seq-lm"]:
     UpperCamelCase			  =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
         lowerCamelCase_   ,   batch_size=lowerCamelCase_   ,   seq_length=lowerCamelCase_   ,   is_pair=lowerCamelCase_   ,   framework=lowerCamelCase_				)

    else:
     UpperCamelCase			  =      self._generate_dummy_inputs_for_causal_lm(
         lowerCamelCase_   ,   batch_size=lowerCamelCase_   ,   seq_length=lowerCamelCase_   ,   is_pair=lowerCamelCase_   ,   framework=lowerCamelCase_				)

    return common_inputs







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    if self.task in ["default", "seq2seq-lm"]:
     UpperCamelCase			  =      super()._flatten_past_key_values_(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)
    else:
     UpperCamelCase			  =      super(lowerCamelCase_   ,   self				)._flatten_past_key_values_(
         lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)







   @property
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    return 1E-4

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _SCREAMING_SNAKE_CASE 		=      """0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	int:







 '''simple docstring'''
 if rng is None:
  UpperCamelCase			  =      random.Random()

 UpperCamelCase			  =      1
 for dim in shape:
  total_dims *= dim

 UpperCamelCase			  =      []
 for _ in range(UpperCamelCase_       ):
  values.append(rng.randint(0							,					vocab_size - 1       )       )

 UpperCamelCase			  =      np.array(UpperCamelCase_							,					dtype=jnp.intaa       ).reshape(UpperCamelCase_       )

 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=None       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      ids_tensor(UpperCamelCase_							,					vocab_size=2							,					rng=UpperCamelCase_       )
 # make sure that at least one token is attended to for each batch
 UpperCamelCase			  =      1
 return attn_mask
@require_flax
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				()







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

    # cut to half length & take max batch_size 3
    UpperCamelCase			  =      2
    UpperCamelCase			  =      inputs["""input_ids"""].shape[-1] // 2
    UpperCamelCase			  =      inputs["""input_ids"""][:max_batch_size, :sequence_length]

    UpperCamelCase			  =      jnp.ones_like(lowerCamelCase_				)
    UpperCamelCase			  =      attention_mask[:max_batch_size, :sequence_length]

    # generate max 5 tokens
    UpperCamelCase			  =      input_ids.shape[-1] + 5
    if config.eos_token_id is not None and config.pad_token_id is None:
     # hack to allow generate for models such as GPT2 as is done in `generate()`
     UpperCamelCase			  =      config.eos_token_id
    return config, input_ids, attention_mask, max_length







   @is_pt_flax_cross_test
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
     UpperCamelCase			  =      getattr(lowerCamelCase_   ,   lowerCamelCase_				)
     UpperCamelCase			  =      pt_model_class(lowerCamelCase_				).eval()
     UpperCamelCase			  =      load_flax_weights_in_pytorch_model(lowerCamelCase_   ,   flax_model.params				)

     UpperCamelCase			  =      flax_model.generate(lowerCamelCase_				).sequences
     UpperCamelCase			  =      pt_model.generate(torch.tensor(lowerCamelCase_   ,   dtype=torch.long				)				)

     if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
      UpperCamelCase			  =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

     self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,   flax_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[0]   ,   input_ids.shape[0] * config.num_return_sequences				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0.8
    UpperCamelCase			  =      10
    UpperCamelCase			  =      0.3
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      2
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)





@require_flax
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
    UpperCamelCase			  =      FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

    UpperCamelCase			  =      """Hello world"""
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   return_tensors="""np"""				).input_ids

    # typos are quickly detected (the correct argument is `do_sample`)
    with self.assertRaisesRegex(lowerCamelCase_   ,   """do_samples"""				):
     model.generate(lowerCamelCase_   ,   do_samples=lowerCamelCase_				)

    # arbitrary arguments that will not be used anywhere are also not accepted
    with self.assertRaisesRegex(lowerCamelCase_   ,   """foo"""				):
     UpperCamelCase			  =      {"""foo""": """bar"""}
     model.generate(lowerCamelCase_   ,   **lowerCamelCase_				)
